Retail Food Environment Intervention Planning: Interviews With Owners and Managers of Small- and Medium-Sized Rural Food Stores
Bibliographic record
Abstract
Retail food environments are an important setting for promoting healthier diets and reducing the global burden of diet-related disease. The purpose of this 2-year community-university partnership was to develop a health promotion intervention for stores in a rural and remote region of British Columbia, Canada. This article reports on the qualitative interviews that were conducted with retail operators as part of an intervention planning process. Seven in-depth, semistructured interviews were conducted with store owners and managers of small- and medium-sized stores in a rural and remote region. Interviews were analyzed using thematic analysis to identify business operations and practices relevant to intervention planning and implementation. Relevant considerations for health promotion planners included the unique business models of rural stores; the prominence of regional travel and "outshopping" in rural and remote regions; challenges balancing between choice, value, and profitability; relationships with suppliers; and using local products to attract and retain customers. Involving retailers in settings-based approaches to improve population nutrition may help to mobilize existing practices and ensure that interventions are responsive to local context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".